Sentence examples for matrix recovery problem from inspiring English sources

Under this context, the problem is converted into a low-rank matrix recovery problem.

In the second stage, discarding the mixed-norm strategy in JSR, the problem of finding the support samples is converted into a low-rank matrix recovery problem.

Additionally, we also aggregate the prediction models for different saliency labels into a matrix, and solve saliency ranking via a low-rank matrix recovery problem.

The problem of seeking the support samples converts to a low-rank matrix recovery problem; meanwhile, the low-rank matrix recovery algorithm could directly obtain the proper sparse representation coefficients on support samples.

However, in practice, the observed data in the low-rank matrix recovery problem may be contaminated with noise, namely (b = mathrm{A}X + e), where e contains measurement errors dominated by certain normal distribution.

The low-rank matrix recovery problem has been a research hotpot recently [1, 2], and it has a range of applications in many fields such as signal or image processing [3, 4], subspace segmentation [5], collaborative filtering [6], and system identification [7].

Based on a useful decomposition of (D^{dagger} - A^{dagger}), this paper reviewed the previous work and provided two sharp lower bounds for the low-rank matrices recovery problem with a unitarily invariant norm.

The proposed algorithm, which is based on row-sparse matrix recovery for DOA estimation problem, not only handles multiple measurements but also converges significantly faster in comparison to other conventional compressed sensing algorithms.

Then we consider sparse signal recovery problem with the random basis matrix in the presence of noise.

In this article, we proposed an effective weighted ℓ2,1 minimization algorithm that exploits the relationship between the noise subspace and the overcomplete basis matrix to obtain the weights for the jointly-sparse signal recovery problem.

In addition to the design of detection algorithm, the selection of filter coefficient matrix also greatly affects the detection performance due to the essential jointly sparse recovery problem.